Update app.py

app.py

@@ -1,93 +1,53 @@
-# app.py
-# Prompt Image Editor — Hugging Face Space
-# Minimal branding in source so the repo can be published under a subsidiary page
-
-
 import os
-import gradio as gr
-from PIL import Image
 import torch
-from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline
-from transformers import logging
-
-
-logging.set_verbosity_error()
-
+import gradio as gr
+from diffusers import StableDiffusionPipeline
 
-# Environment settings (Spaces: Variables & Secrets)
-MODEL_ID = os.getenv("MODEL_ID", "runwayml/stable-diffusion-v1-5")
-HF_TOKEN = os.getenv("HF_API_TOKEN") # set as a Secret in your Space if required
+MODEL_ID = os.getenv("MODEL_ID", "stabilityai/stable-diffusion-2-1")
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
-
-def load_pipelines():
-print(f"Loading model: {MODEL_ID} on {DEVICE}")
-if "inpaint" in MODEL_ID or "img2img" in MODEL_ID:
-pipe = StableDiffusionInpaintPipeline.from_pretrained(
-MODEL_ID,
-revision="fp16",
-torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
-use_auth_token=HF_TOKEN if HF_TOKEN else None,
+# -------------------------
+# Load Model
+# -------------------------
+def load_pipeline():
+    print(f"Loading model: {MODEL_ID} on {DEVICE}")
+    pipe = StableDiffusionPipeline.from_pretrained(
+        MODEL_ID,
+        torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32
+    )
+    pipe = pipe.to(DEVICE)
+    return pipe
+
+pipe = load_pipeline()
+
+# -------------------------
+# Inference Function
+# -------------------------
+def generate(prompt):
+    if not prompt or prompt.strip() == "":
+        return "Please enter a valid prompt.", None
+
+    print("Running inference...")
+
+    result = pipe(
+        prompt=prompt,
+        num_inference_steps=25,
+        guidance_scale=7.5
+    )
+
+    image = result.images[0]
+    return f"Generated image for: {prompt}", image
+
+# -------------------------
+# Gradio UI
+# -------------------------
+interface = gr.Interface(
+    fn=generate,
+    inputs=gr.Textbox(label="Prompt", placeholder="Enter your image prompt..."),
+    outputs=[gr.Textbox(label="Status"), gr.Image(label="Generated Image")],
+    title="Prompt Image Editor",
+    description="Generate AI images using text prompts.",
 )
-else:
-pipe = StableDiffusionPipeline.from_pretrained(
-MODEL_ID,
-revision="fp16",
-torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
-use_auth_token=HF_TOKEN if HF_TOKEN else None,
-)
-if DEVICE == "cuda":
-pipe = pipe.to("cuda")
-return pipe
-
-
-pipe = load_pipelines()
-
-
-def generate_image(prompt: str, negative_prompt: str, steps: int, guidance: float):
-if not prompt:
-return None
-with torch.autocast("cuda") if DEVICE == "cuda" else torch.no_grad():
-out = pipe(prompt=prompt, guidance_scale=guidance, num_inference_steps=steps)
-return out.images[0]
-
-
-
-
-def edit_image(init_image, mask, prompt: str, negative_prompt: str, steps: int, guidance: float):
-if init_image is None:
-return None
-if mask is None:
-return None
-init_img = init_image.convert("RGB")
-mask_img = mask.convert("L")
-with torch.autocast("cuda") if DEVICE == "cuda" else torch.no_grad():
-out = pipe(prompt=prompt, image=init_img, mask_image=mask_img, guidance_scale=guidance, num_inference_steps=steps)
-return out.images[0]
-
-
-with gr.Blocks(title="Prompt Image Editor") as demo:
-gr.Markdown("# Prompt Image Editor")
-with gr.Row():
-with gr.Column(scale=2):
-mode = gr.Radio(["Generate", "Edit / Inpaint"], value="Generate", label="Mode")
-prompt = gr.Textbox(lines=3, label="Prompt")
-negative_prompt = gr.Textbox(lines=2, label="Negative prompt (optional)")
-steps = gr.Slider(minimum=10, maximum=60, step=5, value=28, label="Steps")
-guidance = gr.Slider(minimum=1.0, maximum=20.0, step=0.5, value=7.5, label="Guidance Scale")
-run = gr.Button("Run")
-with gr.Column(scale=3):
-input_image = gr.Image(type="pil", label="Initial image (for editing)")
-mask_image = gr.Image(type="pil", label="Mask (white = edit)")
-output = gr.Image(label="Output")
-
 
-def _run(mode, prompt, negative_prompt, steps, guidance, input_image, mask_image):
-try:
-if mode == "Generate":
-return generate_image(prompt, negative_prompt, steps, guidance)
-else:
-return edit_image(input_image, mask_image, prompt, negative_prompt, steps, guidance)
-except Exception as e:
-return Image.new('RGB', (512,512), color=(255,0,0))
-demo.launch()
+if __name__ == "__main__":
+    interface.launch()